The drastic expansion of consumer electronics (like personal computers, touch pads, smart phones, etc.) creates many human-machine interfaces and multiple types of interactions between human and electronics. Considering the high frequency of such operations in our daily life, an extraordinary amount of biomechanical energy from typing or pressing buttons is available. In this study, we have demonstrated a highly flexible triboelectric nanogenerator (TENG) solely made from elastomeric materials as a cover on a conventional keyboard to harvest biomechanical energy from typing. A dual-mode working mechanism is established with a high transferred charge density of ∼140 μC/m(2) due to both structural and material innovations. We have also carried out fundamental investigations of its performance dependence on various structural factors for optimizing the electric output in practice. The fully packaged keyboard-shaped TENG is further integrated with a horn-like polypyrrole-based supercapacitor as a self-powered system. Typing in normal speed for 1 h, ∼8 × 10(-4) J electricity could be stored, which is capable of driving an electronic thermometer/hydrometer. Our keyboard cover also performs outstanding long-term stability, water resistance, as well as insensitivity to surface conditions, and the last feature makes it useful to research the typing behaviors of different people.
Accurate estimation of the state of charge (SOC) is critical for the normal use of lithium-ion battery equipment like electric vehicles. However, the SOC of lithium-ion battery is not available by direct measure, but can only indirectly be estimated by measurable variables. According to the nonlinear characteristics between the measured values and SOC during the working period of lithium-ion batteries, we propose a method to estimate the SOC of lithium-ion batteries with Temporal Convolutional Network (TCN). The measured values of voltage, current, and temperature during the use of lithium-ion batteries can be directly mapped to accurate SOC in this method without using a battery model or adaptive filter. The network can self-learning and update parameters by being fed datasets collected under various working conditions and then obtain a model that can correctly estimate SOC under different estimation conditions. In addition, it can also be applied to different types of lithium-ion batteries through transfer learning with only a small amount of battery data. At various ambient temperature conditions, the average MAE estimated by the proposed method is 0.67% for all the tests, which proves that the TCN network is an effective tool to estimate the SOC of lithium-ion batteries.
Interrupted sampling repeater jamming (ISRJ) is an effective coherent jamming, which can induce false high-resolution range profile (HRRP) and image to greatly complicate the target detection and recognition of wideband radar. To counter ISRJ, an efficient filter is designed based on the time-frequency (TF) characteristic differences between target echo and ISRJ. Meanwhile, a feasible jamming suppression scheme is proposed, which not only could eliminate ISRJ but also could reserve the target echo. The main emphasis is that the received echo suppressed by our scheme is similar with the true target's HRRP on the basis of high correlation coefficient (CC) and large signal-jamming-to-noise ratio (SJNR) improvement factor. Simulation results are covered to illustrate the feasibility and validness of jamming suppression.
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